To investigate the impact of the proposed regularization approach, we chose three environments from PyGames[1]} and MinAtar [2]}: Catcher, Pixelcopter and Asterix. We reused all the hyper-parameter settings from [3]} except the number of neural networks, which we limited to four and trained each solution for five fixed seeds. For the regularization weight \(\lambda \) , we chose the best value from \(\lbrace 10^{-5}, 10^{-6}, 10^{-7}, 10^{-8}\rbrace \) . The complete list of training parameters can be found in sec:hyperparameters.
